The relation between the producer and consumer price indices: a two-country study

© 2017, Macmillan Publishers Ltd., part of Springer Nature. Marketing managers are often in a dilemma about which pricing index to rely on while calculating the annual increase in the prices for their product. To provide insights that can reduce this dilemma, a critical comparison of the Producer Price index and consumer price index is called for. In this study, the relation between the Producer Price Index (PPI) and Consumer Price Index (CPI) was investigated through a comparison between Turkey and UK. Unlike many other previous studies, this study tried to determine the dominant pricing approach in an economy by examining the relation between the producer and consumer prices. In this context, VAR, impulse-response, variance decomposition, and Granger causality tests were used for the analyses of time series data. The results of study showed that there was bidirectional causality between the producer and consumer prices in both countries. Therefore, it was asserted that businesses in both countries generally apply mixed pricing approach. The results thus provide some interesting insights that can aid marketing managers in their pricing decisions

Factors affecting innovation and imitation of ICT in the agrifood sector

Diffusion of innovations has gained a lot of attention and concerns different scientific fields. Many studies, which examine the determining factors of technological innovations in the agricultural and agrifood sector, have been conducted using the widely used Technology Accepted Model, for a random sample of farmers or firms engaged in agricultural sector. In the present study, a holistic examination of the determining factors that affect the propensity of firms to innovate or imitate, is conducted. The diffusion of ICT tools of firms which are engaged in the NACE 02/03 as well as in the NACE 10/11 classifications for 49 heterogeneous national markets is examined, using the Bass model. The innovation parameter is positively associated with rural income, female employment, export activity and education of farmers, while the imitation parameter is increased in countries whose societies are characterized by uncertainty avoidance

Revisiting the five-facet structure of mindfulness

The current study aimed to replicate the development of the Five-Facet Mindfulness Questionnaire (FFMQ) in a sample of 399 undergraduate students. We factor analyzed the Mindful Attention and Awareness Questionnaire (MAAS), the Freiburg Mindfulness Scale, the Southampton Mindfulness Questionnaire (SMQ), the Cognitive Affective Mindfulness Scale Revised (CAMS-R), and the Kentucky Inventory of Mindfulness Skills (KIMS), but also extended the analysis by including a conceptually related measure, the Philadelphia Mindfulness Scale (PHLMS), and a conceptually unrelated measure, the Langer Mindfulness Scale (LMS). Overall, we found a partial replication of the five-factor structure, with the exception of non-reacting and non-judging which formed a single factor. The PHLMS items loaded as expected with theoretically related factors, whereas the LMS items emerged as separate factor. Finally, we found a new factor that was mostly defined by negatively worded items indicating possible item wording artifacts within the FFMQ. Our conceptual validation study indicates that some facets of the FFMQ can be recovered, but item wording factors may threaten the stability of these facets. Additionally, measures such as the LMS appear to measure not only theoretically, but also empirically different constructs

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods